We propose a nonparametric method for dynamic prediction in event history analysis with high-dimensional, time-dependent covariates. The approach estimates future conditional hazards by combining landmarking supermodels with gradient boosted trees. Unlike joint modeling or Cox landmarking models, the proposed estimator flexibly captures interactions and nonlinear effects without imposing restrictive parametric assumptions or requiring the covariate process to be Markovian. We formulate the approach as a sieve M-estimator and establish weak consistency. Computationally, the problem reduces to a Poisson regression, allowing implementation via standard gradient boosting software. A key theoretical advantage is that the method avoids the temporal inconsistencies that arise in landmark Cox models. Simulation studies demonstrate that the method performs well in a variety of settings, and its practical value is illustrated through an analysis of primary biliary cirrhosis data.

翻译：本文提出了一种用于高维时间依赖性协变量事件历史分析的非参数动态预测方法。该方法通过结合地标超模型与梯度提升树来估计未来条件风险。与联合建模或Cox地标模型不同，所提出的估计器能够灵活捕捉交互效应和非线性效应，无需施加限制性参数假设或要求协变量过程满足马尔可夫性。我们将该方法构建为筛M估计量并证明其弱相合性。在计算层面，该问题可简化为泊松回归，从而可通过标准梯度提升软件实现。一个关键的理论优势在于该方法避免了地标Cox模型中出现的时序不一致问题。模拟研究表明该方法在多种设定下表现良好，并通过原发性胆汁性肝硬化数据分析展示了其实用价值。